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SUMMARY:Hendawy\, M. (CM) - Autonoming Child Online Safety in the Age of AI: From Control to Digital Co-Agency Across Cultures
DESCRIPTION:Children’s lives are now inextricably linked with AI-driven digital systems that shape learning\, social interaction\, and development. This has elevated child online safety to a central concern for families\, policymakers\, and educators. This makes Child online safety a wicked socio-technical problem\, emerging from the complex interplay of social norms\, platform incentives\, cultural expectations\, and rapidly evolving technologies. Dominant control-based paradigms—monitoring\, blocking\, and surveillance—undermine children’s developmental capacity\, erode family trust\, and foreclose the iterative cycles of self regulated learning necessary for digital resilience. This proposal advances digital co-agency as a new paradigm for child online safety. It reframes safety from an outcome of unilateral control to a shared\, relational practice distributed across children\, caregivers\, technologies\, and governance structures. To be effective\, digital co-agency must be grounded in a clear normative standard. I define this standard as ethical safety: protection is legitimate only when it is rights-respecting and developmentally supportive. Within this boundary\, the dissertation proposes autonoming as a design stance for AI-mediated safety systems. Autonoming systems act as developmental mentors that support children’s judgment over time through explanation\, negotiation\, and graduated support that can fade as competence grows. Autonoming is grounded in Self-Regulated Learning (SRL) as the developmental mechanism for durable safety capacity. SRL models learning as cyclical forethought (planning)\, performance (in-the-moment regulation)\, and reflection (evaluating outcomes). The dissertation adopts a socio-technical interpretivist stance and a Design Science Research orientation to produce actionable artifacts that are theoretically grounded and evaluable.. Its core methodological contribution is localization-first comparative design across Cairo and Berlin. This comparative structure helps distinguish between: localized variables (culturally specific norms regarding authority\, privacy\, risk\, norms\, expectations\, and legitimacy conditions that must be adapted to) from ethical invariants (accountability\, contestability\, proportionality that should hold across contexts). \nEvent Host: Mennatullah Hendawy\, Ph.D. Student\, Computational Media  \nAdvisor: Magy Seif El-Nasr \nZoom- https://ucsc.zoom.us/j/93831600031?pwd=hsnX574bcXVQRZa16sKbX0u7OuaMlu.1 \nPasscode-459844
URL:https://events.ucsc.edu/event/hendawy-m-cm-autonoming-child-online-safety-in-the-age-of-ai-from-control-to-digital-co-agency-across-cultures/
LOCATION:
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260309T100000
DTEND;TZID=America/Los_Angeles:20260309T110000
DTSTAMP:20260602T110006
CREATED:20260303T174856Z
LAST-MODIFIED:20260303T174856Z
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SUMMARY:Robbins\, A. (ECE) - How to train your organoid: goal-directed learning in biological neural networks
DESCRIPTION:Artificial neural networks can now learn to play games\, control robots\, generate language\, and solve complicated reasoning tasks\, yet we still lack a clear understanding of how to directly guide learning in biological neural networks. We show that brain organoids can learn to solve a fundamental control task\, balancing an inverted pendulum\, through closed-loop electrophysiology. Cortical organoids interfaced with high-density microelectrode arrays received sensory input about the pole’s angle and produced motor output through their neural activity. Training signals selected by a reinforcement learning algorithm significantly outperformed random stimulation and no-stimulation controls. Blocking glutamatergic transmission abolished the learning and washout restored it\, confirming the adaptation depends on synaptic plasticity. To support this work and future experiments\, we developed BrainDance\, an open-source framework for running reproducible biological learning experiments\, and contributed to RT-Sort\, a real-time spike sorting algorithm. This dissertation presents the tools\, experiments\, and findings from pursuing goal-directed learning in biological neural networks. BrainDance makes these experiments easy-to-create\, reproducible and shareable\, letting any lab with compatible hardware start training their own organoids. \nEvent Host: Ash Robbins\, Ph.D. Candidate\, Electrical and Computer Engineering  \nAdvisor: Mircea Teodorescu \nZoom- https://ucsc.zoom.us/j/95839863615?pwd=EmqTWPN9RRBYZRW7rcpoaT9kqacfRP.1 \nPasscode- 069118
URL:https://events.ucsc.edu/event/robbins-a-ece-how-to-train-your-organoid-goal-directed-learning-in-biological-neural-networks/
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CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260309T140000
DTEND;TZID=America/Los_Angeles:20260309T160000
DTSTAMP:20260602T110006
CREATED:20260303T174216Z
LAST-MODIFIED:20260303T174216Z
UID:10009384-1773064800-1773072000@events.ucsc.edu
SUMMARY:Harrison\, D. (CS) - Multi-Level Control in Neural Dialogue Generation: Style\, Semantics\, and Selection through Over-Generation and Ranking
DESCRIPTION:End-to-end neural generation models have largely displaced the modular architectures that once gave dialogue system designers explicit control over what is said and how it is said. While these models produce fluent text\, they collapse content planning\, sentence planning\, and surface realization into a single undifferentiated decoding step\, sacrificing the controllable structure that earlier systems provided. This dissertation investigates how that structure can be recovered through the over-generate-and-rank (OGR) paradigm: generating multiple candidate outputs and selecting among them using learned or prompt-based ranking functions that jointly optimize semantic fidelity\, stylistic appropriateness\, and conversational coherence. We instantiate OGR at three levels of natural language generation for dialogue: utterance-level stylistic control\, cross-domain semantic evaluation\, and dialogue-level response selection. \nFirst\, we show that explicit conditioning mechanisms\, specifically decoder-level side constraints for personality variation and discourse contrast\, re-introduce stylistic control into neural sequence-to-sequence models without compromising semantic accuracy. Second\, we demonstrate that prompt-based learning with structured linguistic profiles achieves near-perfect personality accuracy and effectively zero slot error rate when combined with ranking\, establishing that LLM prompting with explicit pragmatic specifications can match or exceed fine-tuning for personality-conditioned generation. Third\, we develop a cross-domain semantic error rate evaluation framework that frames slot error computation as an extraction task\, using a LoRA-adapted language model to extract meaning representations from generated text and a trained ranker to select among candidate extractions\, achieving reliable evaluation across 23 topic domains without domain-specific rules. Fourth\, we build and evaluate a speaker-aware transformer response ranker for Athena\, our Alexa Prize socialbot\, demonstrating that learned ranking over heterogeneous generator pools produces significantly longer conversations and higher user ratings than heuristic rule-based selection in a live A/B study with over 6\,000 conversations. \nA unifying finding emerges across all four contributions: the pragmatic features that control personality style in generation—acknowledgements\, engagement questions\, hedges\, exclamations—are the same features that distinguish high-quality from mediocre responses in open-domain dialogue. This parallel reveals that stylistic control and response ranking are complementary mechanisms for achieving the same goal: making dialogue systems sound more natural and engaging. Together\, these results support the dissertation’s central hypothesis that over-generate-and-rank provides a general\, extensible mechanism for controllable neural language generation\, restoring explicit decision points where competing communicative objectives can be weighed. The ranking function serves a role analogous to the sentence planner in classical NLG architectures\, but operates on the outputs of modern neural and LLM-based generators. \n  \nEvent Host: Davan Harrison\, Ph.D. Candidate\, Computer Science \nAdvisor: Marilyn Walker \n 
URL:https://events.ucsc.edu/event/harrison-d-cs-multi-level-control-in-neural-dialogue-generation-style-semantics-and-selection-through-over-generation-and-ranking/
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CATEGORIES:Ph.D. Presentations
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